vLLM x Snowflake Meetup (Wednesday, November 13th, 5:30-8PM PT) at Snowflake HQ, San Mateo
We are excited to announce the last in-person vLLM meetup of the year!
Join the vLLM developers and engineers from Snowflake AI Research to chat about the latest LLM inference optimizations and your 2025 vLLM wishlist!
Register here and be a part of the event!
Latest News 🔥
[2024/10] We have just created a developer slack (slack.vllm.ai) focusing on coordinating contributions and discussing features. Please feel free to join us there!
[2024/10] Ray Summit 2024 held a special track for vLLM! Please find the opening talk slides from the vLLM team here. Learn more from the talks from other vLLM contributors and users!
[2024/07] In partnership with Meta, vLLM officially supports Llama 3.1 with FP8 quantization and pipeline parallelism! Please check out our blog post here.
[2024/06] We hosted the fourth vLLM meetup with Cloudflare and BentoML! Please find the meetup slides here.
[2023/08] We would like to express our sincere gratitude to Andreessen Horowitz (a16z) for providing a generous grant to support the open-source development and research of vLLM.
Optimized CUDA kernels, including integration with FlashAttention and FlashInfer.
Speculative decoding
Chunked prefill
Performance benchmark: We include a performance benchmark at the end of our blog post. It compares the performance of vLLM against other LLM serving engines (TensorRT-LLM, SGLang and LMDeploy). The implementation is under nightly-benchmarks folder and you can reproduce this benchmark using our one-click runnable script.
vLLM is flexible and easy to use with:
Seamless integration with popular Hugging Face models
High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
Tensor parallelism and pipeline parallelism support for distributed inference
Streaming outputs
OpenAI-compatible API server
Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron.
Prefix caching support
Multi-lora support
vLLM seamlessly supports most popular open-source models on HuggingFace, including:
We welcome and value any contributions and collaborations.
Please check out CONTRIBUTING.md for how to get involved.
Sponsors
vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
a16z
AMD
Anyscale
AWS
Crusoe Cloud
Databricks
DeepInfra
Dropbox
Google Cloud
Lambda Lab
NVIDIA
Replicate
Roblox
RunPod
Sequoia Capital
Skywork AI
Trainy
UC Berkeley
UC San Diego
ZhenFund
We also have an official fundraising venue through OpenCollective. We plan to use the fund to support the development, maintenance, and adoption of vLLM.
Citation
If you use vLLM for your research, please cite our paper:
@inproceedings{kwon2023efficient,
title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
year={2023}
}
Contact Us
For technical questions and feature requests, please use Github issues or discussions.
For discussing with fellow users, please use Discord.
For coordinating contributions and development, please use Slack.
For security disclosures, please use Github’s security advisory feature.
For collaborations and partnerships, please contact us at vllm-questions AT lists.berkeley.edu.
Easy, fast, and cheap LLM serving for everyone
| Documentation | Blog | Paper | Discord | Twitter/X | Developer Slack |
vLLM x Snowflake Meetup (Wednesday, November 13th, 5:30-8PM PT) at Snowflake HQ, San Mateo
We are excited to announce the last in-person vLLM meetup of the year! Join the vLLM developers and engineers from Snowflake AI Research to chat about the latest LLM inference optimizations and your 2025 vLLM wishlist! Register here and be a part of the event!
Latest News 🔥
About
vLLM is a fast and easy-to-use library for LLM inference and serving.
vLLM is fast with:
Performance benchmark: We include a performance benchmark at the end of our blog post. It compares the performance of vLLM against other LLM serving engines (TensorRT-LLM, SGLang and LMDeploy). The implementation is under nightly-benchmarks folder and you can reproduce this benchmark using our one-click runnable script.
vLLM is flexible and easy to use with:
vLLM seamlessly supports most popular open-source models on HuggingFace, including:
Find the full list of supported models here.
Getting Started
Install vLLM with
pip
or from source:Visit our documentation to learn more.
Contributing
We welcome and value any contributions and collaborations. Please check out CONTRIBUTING.md for how to get involved.
Sponsors
vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
We also have an official fundraising venue through OpenCollective. We plan to use the fund to support the development, maintenance, and adoption of vLLM.
Citation
If you use vLLM for your research, please cite our paper:
Contact Us